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Luoyadan / CRB-active-3Ddet

The official PyTorch implementation of "Exploring Active 3D Object Detection from a Generalization Perspective" (ICLR Spotlight 2023).
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Error occured after pre #15

Open yux326 opened 12 months ago

yux326 commented 12 months ago

Hello! Thanks for your work! I'm faced with a trouble and hope you could help. I'm trying to run train a model and I set batch_size=1 and epoches=20 by running

python` train.py --cfg_file cfgs/active-kitti_models/pv_rcnn_active_crb.yaml --batch_size 1 --epochs 20

And my config of active learning is:

ACTIVE_TRAIN: METHOD: crb AGGREGATION: mean PRE_TRAIN_SAMPLE_NUMS: 100
PRE_TRAIN_EPOCH_NUMS: 40 TRAIN_RESUME: False SELECT_NUMS: 100 SELECT_LABEL_EPOCH_INTERVAL: 40 TOTAL_BUDGET_NUMS: 600 ACTIVE_CONFIG: K1: 5 K2: 3 BANDWIDTH: 5 CLUSTERING: kmeans++

The pre-train is finished without any error, but after this an error occured before the train start:

2023-11-16 02:15:08,630 INFO **Start training active-kitti_models/pv_rcnn_active_crb(select-100)**
2023-11-16 02:15:08,631 INFO Start Active Pre-train train: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [02:13<00:00, 1.33s/it, total_it=4000] epochs: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [1:28:31<00:00, 132.79s/it, loss=1.66, lr=0.00252] 2023-11-16 03:43:40,420 INFO Complete Active Pre-train 2023-11-16 03:43:40,421 INFO Start Active Train Loop epochs: 0%| | 0/240 [00:00<?, ?it/s found and enabled 3 Dropout layers for random sampling | 0/3612 [00:00<?, ?it/s] evaluating_unlabelled_set_epoch_40: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3612/3612 [12:53<00:00, 4.67it/s] inf_grads_unlabelled_set_epoch_40: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 500/500 [10:11<00:00, 1.22s/it] --- {kmeans++} running time: 54.5201575756073 seconds for fc grads---████████████████████████████████████████████████████████████████████████████████████████| 500/500 [10:11<00:00, 1.18s/it] --- Build the uniform distribution running time: 0.04799032211303711 seconds --- global_density_div_for_epoch_40: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [04:52<00:00, 2.92s/it] successfully saved selected frames for epoch 40 for rank 0███████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [04:52<00:00, 3.49s/it] wandb: WARNING Step must only increase in log calls. Step 4000 < 4001; dropping {'active_selection/num_bbox_Car': tensor(113, device='cuda:0')}. wandb: WARNING Step must only increase in log calls. Step 4000 < 4001; dropping {'active_selection/mean_points_Car': tensor(141.4796)}. wandb: WARNING Step must only increase in log calls. Step 4000 < 4001; dropping {'active_selection/median_points_Car': tensor(99.4700)}. wandb: WARNING Step must only increase in log calls. Step 4000 < 4001; dropping {'active_selection/variance_points_Car': tensor(22668.2227)}. wandb: WARNING Step must only increase in log calls. Step 4000 < 4001; dropping {'active_selection/num_bbox_Pedestrian': tensor(119, device='cuda:0')}. wandb: WARNING Step must only increase in log calls. Step 4000 < 4001; dropping {'active_selection/mean_points_Pedestrian': tensor(171.6909)}. wandb: WARNING Step must only increase in log calls. Step 4000 < 4001; dropping {'active_selection/median_points_Pedestrian': tensor(163.9500)}. wandb: WARNING Step must only increase in log calls. Step 4000 < 4001; dropping {'active_selection/variance_points_Pedestrian': tensor(2096.7180)}. wandb: WARNING Step must only increase in log calls. Step 4000 < 4001; dropping {'active_selection/num_bbox_Cyclist': tensor(46, device='cuda:0')}. wandb: WARNING Step must only increase in log calls. Step 4000 < 4001; dropping {'active_selection/mean_points_Cyclist': tensor(24.0983)}.
wandb: WARNING Step must only increase in log calls. Step 4000 < 4001; dropping {'active_selection/median_points_Cyclist': tensor(20.5300)}.
wandb: WARNING Step must only increase in log calls. Step 4000 < 4001; dropping {'active_selection/variance_points_Cyclist': tensor(989.8525)}.
wandb: WARNING Step must only increase in log calls. Step 4000 < 4001; dropping {'active_selection/total_bbox_selected': tensor(278, device='cuda:0')}.
Batch_size of a single loader: 1
2023-11-16 04:12:39,231 INFO Database filter by min points Car: 14357 => 13532
2023-11-16 04:12:39,233 INFO Database filter by min points Pedestrian: 2207 => 2168
2023-11-16 04:12:39,233 INFO Database filter by min points Cyclist: 734 => 705 2023-11-16 04:12:39,286 INFO Database filter by difficulty Car: 13532 => 10759 2023-11-16 04:12:39,295 INFO Database filter by difficulty Pedestrian: 2168 => 2075 2023-11-16 04:12:39,298 INFO Database filter by difficulty Cyclist: 705 => 581 2023-11-16 04:12:39,313 INFO Loading KITTI dataset 2023-11-16 04:12:39,547 INFO Total samples for KITTI dataset: 3712 2023-11-16 04:12:39,812 INFO Database filter by min points Car: 14357 => 13532 2023-11-16 04:12:39,814 INFO Database filter by min points Pedestrian: 2207 => 2168 2023-11-16 04:12:39,814 INFO Database filter by min points Cyclist: 734 => 705 2023-11-16 04:12:39,852 INFO Database filter by difficulty Car: 13532 => 10759 2023-11-16 04:12:39,295 INFO Database filter by difficulty Pedestrian: 2168 => 2075
2023-11-16 04:12:39,298 INFO Database filter by difficulty Cyclist: 705 => 581
2023-11-16 04:12:39,313 INFO Loading KITTI dataset
2023-11-16 04:12:39,547 INFO Total samples for KITTI dataset: 3712 2023-11-16 04:12:39,812 INFO Database filter by min points Car: 14357 => 13532 2023-11-16 04:12:39,814 INFO Database filter by min points Pedestrian: 2207 => 2168 2023-11-16 04:12:39,814 INFO Database filter by min points Cyclist: 734 => 705 2023-11-16 04:12:39,852 INFO Database filter by difficulty Car: 13532 => 10759 2023-11-16 04:12:39,860 INFO Database filter by difficulty Pedestrian: 2168 => 2075 2023-11-16 04:12:39,862 INFO Database filter by difficulty Cyclist: 705 => 581 2023-11-16 04:12:39,873 INFO Loading KITTI dataset 2023-11-16 04:12:40,086 INFO Total samples for KITTI dataset: 3712 2023-11-16 04:12:40,088 INFO Loading KITTI dataset 2023-11-16 04:12:40,342 INFO Total samples for KITTI dataset: 3769 2023-11-16 04:12:40,493 INFO finished selection: reload init weights of the model epochs: 0%| | 0 /240 [29:00<?, ?it/s] Traceback (most recent call last): File "train.py", line 313, in main() File "train.py", line 255, in main dist_train=dist_train File "/home/CRB-active-3Ddet/tools/train_utils/train_active_utils.py", line 322, in train_model_active model.load_state_dict(backbone_init_ckpt, strict=cfg.ACTIVE_TRAIN.METHOD!='llal') # strict=cfg.ACTIVE_TRAIN.METHOD!='llal' File "/opt/conda/envs/crb/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1483, in load_state_dict self.class.name, "\n\t".join(error_msgs))) RuntimeError: Error(s) in loading state_dict for PVRCNN: Missing key(s) in state_dict: "backbone_3d.conv_input.0.weight", "backbone_3d.conv_input.1.weight", "backbone_3d.conv_input.1.bias", "backbone_3d.conv_input.1.running_mean", "backbone_3d.conv_input.1.running_var", "backbon e_3d.conv1.0.0.weight", "backbone_3d.conv1.0.1.weight", "backbone_3d.conv1.0.1.bias", "backbone_3d.conv1.0.1.running_mean", "backbone_3d.conv1.0.1.running_var", "backbone_3d.conv2.0.0.weight", "backbone_3d.conv2.0.1.weight", "back bone_3d.conv2.0.1.bias", "backbone_3d.conv2.0.1.running_mean", "backbone_3d.conv2.0.1.running_var", "backbone_3d.conv2.1.0.weight", "backbone_3d.conv2.1.1.weight", "backbone_3d.conv2.1.1.bias", "backbone_3d.conv2.1.1.running_mean" , "backbone_3d.conv2.1.1.running_var", "backbone_3d.conv2.2.0.weight", "backbone_3d.conv2.2.1.weight", "backbone_3d.conv2.2.1.bias", "backbone_3d.conv2.2.1.running_mean", "backbone_3d.conv2.2.1.running_var", "backbone_3d.conv3.0.0 .weight", "backbone_3d.conv3.0.1.weight", "backbone_3d.conv3.0.1.bias", "backbone_3d.conv3.0.1.running_mean", "backbone_3d.conv3.0.1.running_var", "backbone_3d.conv3.1.0.weight", "backbone_3d.conv3.1.1.weight", "backbone_3d.conv3. 1.1.bias", "backbone_3d.conv3.1.1.running_mean", "backbone_3d.conv3.1.1.running_var", "backbone_3d.conv3.2.0.weight", "backbone_3d.conv3.2.1.weight", "backbone_3d.conv3.2.1.bias", "backbone_3d.conv3.2.1.running_mean", "backbone_3d .conv3.2.1.running_var", "backbone_3d.conv4.0.0.weight", "backbone_3d.conv4.0.1.weight", "backbone_3d.conv4.0.1.bias", "backbone_3d.conv4.0.1.running_mean", "backbone_3d.conv4.0.1.running_var", "backbone_3d.conv4.1.0.weight", "bac kbone_3d.conv4.1.1.weight", "backbone_3d.conv4.1.1.bias", "backbone_3d.conv4.1.1.running_mean", "backbone_3d.conv4.1.1.running_var", "backbone_3d.conv4.2.0.weight", "backbone_3d.conv4.2.1.weight", "backbone_3d.conv4.2.1.bias", "ba ckbone_3d.conv4.2.1.running_mean", "backbone_3d.conv4.2.1.running_var", "backbone_3d.conv_out.0.weight", "backbone_3d.conv_out.1.weight", "backbone_3d.conv_out.1.bias", "backbone_3d.conv_out.1.running_mean", "backbone_3d.conv_out. 1.running_var", "pfe.SA_layers.0.mlps.0.0.weight", "pfe.SA_layers.0.mlps.0.1.weight", "pfe.SA_layers.0.mlps.0.1.bias", "pfe.SA_layers.0.mlps.0.1.running_mean", "pfe.SA_layers.0.mlps.0.1.running_var", "pfe.SA_layers.0.mlps.0.3.weig ht", "pfe.SA_layers.0.mlps.0.4.weight", "pfe.SA_layers.0.mlps.0.4.bias", "pfe.SA_layers.0.mlps.0.4.running_mean", "pfe.SA_layers.0.mlps.0.4.running_var", "pfe.SA_layers.0.mlps.1.0.weight", "pfe.SA_layers.0.mlps.1.1.weight", "pfe.S A_layers.0.mlps.1.1.bias", "pfe.SA_layers.0.mlps.1.1.running_mean", "pfe.SA_layers.0.mlps.1.1.running_var", "pfe.SA_layers.0.mlps.1.3.weight", "pfe.SA_layers.0.mlps.1.4.weight", "pfe.SA_layers.0.mlps.1.4.bias", "pfe.SA_layers.0.ml ps.1.4.running_mean", "pfe.SA_layers.0.mlps.1.4.running_var", "pfe.SA_layers.1.mlps.0.0.weight", "pfe.SA_layers.1.mlps.0.1.weight", "pfe.SA_layers.1.mlps.0.1.bias", "pfe.SA_layers.1.mlps.0.1.running_mean", "pfe.SA_layers.1.mlps.0. 1.running_var", "pfe.SA_layers.1.mlps.0.3.weight", "pfe.SA_layers.1.mlps.0.4.weight", "pfe.SA_layers.1.mlps.0.4.bias", "pfe.SA_layers.1.mlps.0.4.running_mean", "pfe.SA_layers.1.mlps.0.4.running_var", "pfe.SA_layers.1.mlps.1.0.weig ht", "pfe.SA_layers.1.mlps.1.1.weight", "pfe.SA_layers.1.mlps.1.1.bias", "pfe.SA_layers.1.mlps.1.1.running_mean", "pfe.SA_layers.1.mlps.1.1.running_var", "pfe.SA_layers.1.mlps.1.3.weight", "pfe.SA_layers.1.mlps.1.4.weight", "pfe.S A_layers.1.mlps.1.4.bias", "pfe.SA_layers.1.mlps.1.4.running_mean", "pfe.SA_layers.1.mlps.1.4.running_var", "pfe.SA_layers.2.mlps.0.0.weight", "pfe.SA_layers.2.mlps.0.1.weight", "pfe.SA_layers.2.mlps.0.1.bias", "pfe.SA_layers.2.ml ps.0.1.running_mean", "pfe.SA_layers.2.mlps.0.1.running_var", "pfe.SA_layers.2.mlps.0.3.weight", "pfe.SA_layers.2.mlps.0.4.weight", "pfe.SA_layers.2.mlps.0.4.bias", "pfe.SA_layers.2.mlps.0.4.running_mean", "pfe.SA_layers.2.mlps.0. 4.running_var", "pfe.SA_layers.2.mlps.1.0.weight", "pfe.SA_layers.2.mlps.1.1.weight", "pfe.SA_layers.2.mlps.1.1.bias", "pfe.SA_layers.2.mlps.1.1.running_mean", "pfe.SA_layers.2.mlps.1.1.running_var", "pfe.SA_layers.2.mlps.1.3.weig ht", "pfe.SA_layers.2.mlps.1.4.weight", "pfe.SA_layers.2.mlps.1.4.bias", "pfe.SA_layers.2.mlps.1.4.running_mean", "pfe.SA_layers.2.mlps.1.4.running_var", "pfe.SA_layers.3.mlps.0.0.weight", "pfe.SA_layers.3.mlps.0.1.weight", "pfe.S A_layers.3.mlps.0.1.bias", "pfe.SA_layers.3.mlps.0.1.running_mean", "pfe.SA_layers.3.mlps.0.1.running_var", "pfe.SA_layers.3.mlps.0.3.weight", "pfe.SA_layers.3.mlps.0.4.weight", "pfe.SA_layers.3.mlps.0.4.bias", "pfe.SA_layers.3.ml ps.0.4.running_mean", "pfe.SA_layers.3.mlps.0.4.running_var", "pfe.SA_layers.3.mlps.1.0.weight", "pfe.SA_layers.3.mlps.1.1.weight", "pfe.SA_layers.3.mlps.1.1.bias", "pfe.SA_layers.3.mlps.1.1.running_mean", "pfe.SA_layers.3.mlps.1. 1.running_var", "pfe.SA_layers.3.mlps.1.3.weight", "pfe.SA_layers.3.mlps.1.4.weight", "pfe.SA_layers.3.mlps.1.4.bias", "pfe.SA_layers.3.mlps.1.4.running_mean", "pfe.SA_layers.3.mlps.1.4.running_var", "pfe.SA_rawpoints.mlps.0.0.wei ght", "pfe.SA_rawpoints.mlps.0.1.weight", "pfe.SA_rawpoints.mlps.0.1.bias", "pfe.SA_rawpoints.mlps.0.1.running_mean", "pfe.SA_rawpoints.mlps.0.1.running_var", "pfe.SA_rawpoints.mlps.0.3.weight", "pfe.SA_rawpoints.mlps.0.4.weight", "pfe.SA_rawpoints.mlps.0.4.bias", "pfe.SA_rawpoints.mlps.0.4.running_mean", "pfe.SA_rawpoints.mlps.0.4.running_var", "pfe.SA_rawpoints.mlps.1.0.weight", "pfe.SA_rawpoints.mlps.1.1.weight", "pfe.SA_rawpoints.mlps.1.1.bias", "pfe.S A_rawpoints.mlps.1.1.running_mean", "pfe.SA_rawpoints.mlps.1.1.running_var", "pfe.SA_rawpoints.mlps.1.3.weight", "pfe.SA_rawpoints.mlps.1.4.weight", "pfe.SA_rawpoints.mlps.1.4.bias", "pfe.SA_rawpoints.mlps.1.4.running_mean", "pfe. SA_rawpoints.mlps.1.4.running_var", "pfe.vsa_point_feature_fusion.0.weight", "pfe.vsa_point_feature_fusion.1.weight", "pfe.vsa_point_feature_fusion.1.bias", "pfe.vsa_point_feature_fusion.1.running_mean", "pfe.vsa_point_feature_fus ion.1.running_var", "backbone_2d.blocks.0.13.weight", "backbone_2d.blocks.0.14.weight", "backbone_2d.blocks.0.14.bias", "backbone_2d.blocks.0.14.running_mean", "backbone_2d.blocks.0.14.running_var", "backbone_2d.blocks.0.16.weight ", "backbone_2d.blocks.0.17.weight", "backbone_2d.blocks.0.17.bias", "backbone_2d.blocks.0.17.running_mean", "backbone_2d.blocks.0.17.running_var", "point_head.cls_layers.0.weight", "point_head.cls_layers.1.weight", "point_head.cl s_layers.1.bias", "point_head.cls_layers.1.running_mean", "point_head.cls_layers.1.running_var", "point_head.cls_layers.3.weight", "point_head.cls_layers.4.weight", "point_head.cls_layers.4.bias", "point_head.clslayers.4.running mean", "point_head.cls_layers.4.running_var", "point_head.cls_layers.6.weight", "point_head.cls_layers.6.bias", "roi_head.roi_grid_pool_layer.mlps.0.0.weight", "roi_head.roi_grid_pool_layer.mlps.0.1.weight", "roi_head.roi_grid_poo l_layer.mlps.0.1.bias", "roi_head.roi_grid_pool_layer.mlps.0.1.running_mean", "roi_head.roi_grid_pool_layer.mlps.0.1.running_var", "roi_head.roi_grid_pool_layer.mlps.0.3.weight", "roi_head.roi_grid_pool_layer.mlps.0.4.weight", "ro i_head.roi_grid_pool_layer.mlps.0.4.bias", "roi_head.roi_grid_pool_layer.mlps.0.4.running_mean", "roi_head.roi_grid_pool_layer.mlps.0.4.running_var", "roi_head.roi_grid_pool_layer.mlps.1.0.weight", "roi_head.roi_grid_pool_layer.ml ps.1.1.weight", "roi_head.roi_grid_pool_layer.mlps.1.1.bias", "roi_head.roi_grid_pool_layer.mlps.1.1.running_mean", "roi_head.roi_grid_pool_layer.mlps.1.1.running_var", "roi_head.roi_grid_pool_layer.mlps.1.3.weight", "roi_head.roi _grid_pool_layer.mlps.1.4.weight", "roi_head.roi_grid_pool_layer.mlps.1.4.bias", "roi_head.roi_grid_pool_layer.mlps.1.4.running_mean", "roi_head.roi_grid_pool_layer.mlps.1.4.running_var", "roi_head.shared_fclayer.0.weight", "roi head.shared_fc_layer.1.weight", "roi_head.shared_fc_layer.1.bias", "roi_head.shared_fc_layer.1.running_mean", "roi_head.shared_fc_layer.1.running_var", "roi_head.shared_fc_layer.4.weight", "roi_head.shared_fc_layer.5.weight", "roi _head.shared_fc_layer.5.bias", "roi_head.shared_fc_layer.5.running_mean", "roi_head.shared_fc_layer.5.running_var", "roi_head.cls_layers.0.weight", "roi_head.cls_layers.1.weight", "roi_head.cls_layers.1.bias", "roi_head.cls_layers .1.running_mean", "roi_head.cls_layers.1.running_var", "roi_head.cls_layers.4.weight", "roi_head.cls_layers.5.weight", "roi_head.cls_layers.5.bias", "roi_head.cls_layers.5.running_mean", "roi_head.cls_layers.5.running_var", "roi_h ead.cls_layers.7.weight", "roi_head.cls_layers.7.bias", "roi_head.reg_layers.0.weight", "roi_head.reg_layers.1.weight", "roi_head.reg_layers.1.bias", "roi_head.reg_layers.1.running_mean", "roi_head.reg_layers.1.runningvar", "roi head.reg_layers.4.weight", "roi_head.reg_layers.5.weight", "roi_head.reg_layers.5.bias", "roi_head.reg_layers.5.running_mean", "roi_head.reg_layers.5.running_var", "roi_head.reg_layers.7.weight", "roi_head.reg_layers.7.bias". Unexpected key(s) in state_dict: "vfe.pfn_layers.0.linear.weight", "vfe.pfn_layers.0.norm.weight", "vfe.pfn_layers.0.norm.bias", "vfe.pfn_layers.0.norm.running_mean", "vfe.pfn_layers.0.norm.running_var", "vfe.pfn_layers.0. norm.num_batches_tracked", "backbone_2d.blocks.2.1.weight", "backbone_2d.blocks.2.2.weight", "backbone_2d.blocks.2.2.bias", "backbone_2d.blocks.2.2.running_mean", "backbone_2d.blocks.2.2.running_var", "backbone_2d.blocks.2.2.num_b atches_tracked", "backbone_2d.blocks.2.4.weight", "backbone_2d.blocks.2.5.weight", "backbone_2d.blocks.2.5.bias", "backbone_2d.blocks.2.5.running_mean", "backbone_2d.blocks.2.5.running_var", "backbone_2d.blocks.2.5.num_batches_tra cked", "backbone_2d.blocks.2.7.weight", "backbone_2d.blocks.2.8.weight", "backbone_2d.blocks.2.8.bias", "backbone_2d.blocks.2.8.running_mean", "backbone_2d.blocks.2.8.running_var", "backbone_2d.blocks.2.8.num_batches_tracked", "ba ckbone_2d.blocks.2.10.weight", "backbone_2d.blocks.2.11.weight", "backbone_2d.blocks.2.11.bias", "backbone_2d.blocks.2.11.running_mean", "backbone_2d.blocks.2.11.running_var", "backbone_2d.blocks.2.11.num_batches_tracked", "backbo ne_2d.blocks.2.13.weight", "backbone_2d.blocks.2.14.weight", "backbone_2d.blocks.2.14.bias", "backbone_2d.blocks.2.14.running_mean", "backbone_2d.blocks.2.14.running_var", "backbone_2d.blocks.2.14.num_batches_tracked", "backbone_2 d.blocks.2.16.weight", "backbone_2d.blocks.2.17.weight", "backbone_2d.blocks.2.17.bias", "backbone_2d.blocks.2.17.running_mean", "backbone_2d.blocks.2.17.running_var", "backbone_2d.blocks.2.17.num_batches_tracked", "backbone_2d.de blocks.2.0.weight", "backbone_2d.deblocks.2.1.weight", "backbone_2d.deblocks.2.1.bias", "backbone_2d.deblocks.2.1.running_mean", "backbone_2d.deblocks.2.1.running_var", "backbone_2d.deblocks.2.1.num_batches_tracked".

size mismatch for backbone_2d.blocks.0.1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 3, 3]).
size mismatch for backbone_2d.blocks.0.2.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for backbone_2d.blocks.0.2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for backbone_2d.blocks.0.2.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for backbone_2d.blocks.0.2.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for backbone_2d.blocks.0.4.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
size mismatch for backbone_2d.blocks.0.5.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for backbone_2d.blocks.0.5.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for backbone_2d.blocks.0.5.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for backbone_2d.blocks.0.5.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for backbone_2d.blocks.0.7.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
size mismatch for backbone_2d.blocks.0.8.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for backbone_2d.blocks.0.8.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for backbone_2d.blocks.0.8.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for backbone_2d.blocks.0.8.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for backbone_2d.blocks.0.10.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
size mismatch for backbone_2d.blocks.0.11.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for backbone_2d.blocks.0.11.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for backbone_2d.blocks.0.11.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for backbone_2d.blocks.0.11.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for backbone_2d.blocks.1.1.weight: copying a param with shape torch.Size([128, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 128, 3, 3]).
size mismatch for backbone_2d.blocks.1.2.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for backbone_2d.blocks.1.2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for backbone_2d.blocks.1.2.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone_2d.blocks.1.2.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone_2d.blocks.1.4.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]). size mismatch for backbone_2d.blocks.1.5.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone_2d.blocks.1.5.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone_2d.blocks.1.5.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone_2d.blocks.1.5.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone_2d.blocks.1.7.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]). size mismatch for backbone_2d.blocks.1.8.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone_2d.blocks.1.8.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone_2d.blocks.1.8.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone_2d.blocks.1.8.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone_2d.blocks.1.10.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]). size mismatch for backbone_2d.blocks.1.11.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone_2d.blocks.1.11.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone_2d.blocks.1.11.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone_2d.blocks.1.11.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone_2d.blocks.1.13.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]). size mismatch for backbone_2d.blocks.1.14.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone_2d.blocks.1.14.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone_2d.blocks.1.14.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone_2d.blocks.1.14.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone_2d.blocks.1.16.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]). size mismatch for backbone_2d.blocks.1.17.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone_2d.blocks.1.17.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone_2d.blocks.1.17.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for backbone_2d.blocks.1.17.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for backbone_2d.deblocks.0.0.weight: copying a param with shape torch.Size([64, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]). size mismatch for backbone_2d.deblocks.0.1.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for backbone_2d.deblocks.0.1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for backbone_2d.deblocks.0.1.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for backbone_2d.deblocks.0.1.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone_2d.deblocks.1.0.weight: copying a param with shape torch.Size([128, 128, 2, 2]) from checkpoint, the shape in current model is torch.Size([256, 256, 2, 2]). size mismatch for backbone_2d.deblocks.1.1.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone_2d.deblocks.1.1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone_2d.deblocks.1.1.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone_2d.deblocks.1.1.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for dense_head.conv_cls.weight: copying a param with shape torch.Size([18, 384, 1, 1]) from checkpoint, the shape in current model is torch.Size([18, 512, 1, 1]). size mismatch for dense_head.conv_box.weight: copying a param with shape torch.Size([42, 384, 1, 1]) from checkpoint, the shape in current model is torch.Size([42, 512, 1, 1]). size mismatch for dense_head.conv_dir_cls.weight: copying a param with shape torch.Size([12, 384, 1, 1]) from checkpoint, the shape in current model is torch.Size([12, 512, 1, 1]).

It seems that the size of some part of the model goes wrong, while i didn't change anything about the model structure, could you please help find out the solution?

zhuoxiao-chen commented 8 months ago

Have you fixed this issue?